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Found 4 papers, 2 papers with code
Single-Reset Divide & Conquer Imitation Learning
Demonstrations are commonly used to speed up the learning process of Deep Reinforcement Learning algorithms.
Leveraging Sequentiality in Reinforcement Learning from a Single Demonstration
This sequential goal-reaching approach raises a problem of compatibility between successive goals: we need to ensure that the state resulting from reaching a goal is compatible with the achievement of the following goals.
Divide & Conquer Imitation Learning
In such context, Imitation Learning (IL) can be a powerful approach to bootstrap the learning process.
Selection-Expansion: A Unifying Framework for Motion-Planning and Diversity Search Algorithms
In particular, we show empirically that, if the mapping is smooth enough, i. e. if two close policies in the parameter space lead to similar outcomes, then diversity algorithms tend to inherit exploration properties of MP algorithms.
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